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To analyse a very large data set containing lengthy variables, we adopt asequential estimation idea and propose a parallel divide-and-conquer method. Weconduct several conventional sequential estimation procedures separately, andproperly integrate their results while maintaining the desired statisticalproperties. Additionally, using a criterion from the statistical experimentdesign, we adopt an adaptive sample selection, together with an adaptiveshrinkage estimation method, to simultaneously accelerate the estimationprocedure and identify the effective variables. We confirm the cogency of ourmethods through theoretical justifications and numerical results derived fromsynthesized data sets. We then apply the proposed method to three real datasets, including those pertaining to appliance energy use and particulate matterconcentration.

Distributed sequential method for analyzing massive data